Statistical hypothesis tests in wavelet analysis are used to asses the likelihood that time series features are noise. The choice of test will determine what features emerge as a signal. Tests based on area do poorly at distinguishing abrupt fluctuations from periodic behavior unlike tests based on arc length that do better. The application of the tests suggests that there are features in India rainfall time series that emerge from background noise.

The observed profiles of the turbulent energy dissipation rate look so erratic that we can hardly identify them as continuous curves. However, we found that each sequence has the striking feature of self-similarity. Using this, we can efficiently take ensemble statistics of the vertically averaged energy dissipation rate from a single observation profile, by scaling up and promoting the observed value at each depth to one that corresponds to the whole profile.

In this work, we conduct the strongly coupled data assimilation (SCDA) experiments using a two-scale Lorenz '96 model with the ensemble adjustment Kalman filter. This is a coupled system composed by two models with different scales. We have developed a new localization strategy for the cross-domain error covariances, which is crucial for the quality of SCDA. The results show that the SCDA with localization could provide much more accurate estimation of the states than the WCDA.

The relative importance of chaotic stirring and smaller-scale turbulent mixing for the distribution of dye in an idealized ocean flow feature is quantified using three different methods. We find that stirring is the dominant process in large areas with fast stirring, while mixing dominates in small fast-stirring regions and all slow-stirring regions. This quantification of process dominance can help oceanographers think about when to model stirring accurately, which can be costly.

This research intends to characterize the South Atlantic Anomaly (SAA) by applying power spectrum analysis approach. The outcomes of the research revealed that the SAA region had a tendency to be persistent during active period and normal periods. It can be said, it experiences this characteristic because of the Earth’s magnetic field strength. It is very important for spacecraft when entering the SAA take safety precaution in order to minimize the damage.

Data assimilation looks for an optimal way to learn from observations of a dynamical system to improve the quality of its predictions. The goal is to filter out the noise (both observation and model noise) to retrieve the true signal. Among all possible methods, particle filters are promising; the method is fast and elegant, and it allows for a Bayesian analysis. In this review paper, we discuss implementation techniques for (local) particle filters in high-dimensional systems.

This study focuses on the non-Gaussian statistics based on a 10 240-member ensemble data assimilation. The 10 240 members can resolve the detailed structures of the probability density functions (PDFs) and indicates that the non-Gaussian PDF is caused by multimodality and outliers. The results show that the outliers appear randomly and that large multimodality corresponds well with large analysis error, mainly in the tropical regions where highly nonlinear convective processes appear frequently.

Accuracy of numerical weather prediction forecasts is strongly related to the quality of initial conditions employed. To improve them, it seems advantageous to use radar reflectivity observations because of their high spatial and temporal resolution. This is tested in a high-resolution model whose domain covers Italy. Results show that the employment of reflectivity observations improves precipitation forecast accuracy, but the positive impact is lost after a few hours of forecast.

Accurate estimation of subsurface geological parameters is essential for the oil industry. This is done by combining observations with an estimation from a model. Ensemble Kalman filter is a widely used method for inverse modeling, while ensemble transform particle filtering is a recently developed method that has been applied to estimate only a small number of parameters and in fluids. We show that for a high-dimensional inverse problem it is superior to an ensemble Kalman filter.